With the rapid development of 3D laser scanning technology, 3D digital geometric model has become the fourth type of digital media form after digital audio, digital image, and digital video. Its related basic theory and key technology research have been developed into a new discipline—digital geometric processing, and are used widely in the computer-aided design, animation industry, bio-medicine, digital cultural heritage protection and other fields. In addition, with the rise of hardware devices such as Microsoft Kinect and Primesense (a somatosensory technology), the access to 3D (3 Dimensions) point cloud information has become more convenient. In 3D vision, local feature extraction has been the most critical part of point cloud processing. Local feature descriptors are used to describe the local features of the extracted point cloud. Therefore, for both 3D object recognition and 3D reconstruction field, local feature descriptors play a very important role.
At present, the research results of 3D local feature descriptors (and 3D local feature extraction) are basically divided into three categories: one is based on signature, which calculates the signature of a local point cloud to describe its features, including Point Signature, 3D Point Fingerprint, 3D-SURF, etc.; the second one is based on histograms, which calculates the histogram of a local point cloud to describe the local features, including Spin Image and 3D Shape Contexts; the third one is the 3D local feature descriptor SHOT containing signatures and histograms which is recently proposed. The descriptor SHOT combines the advantages of signatures and histograms and can be widely used in 3D point cloud processing.
However, the three 3D local feature descriptors in the prior art ignore the concavo-convex features of the point cloud surface, so that the extracted local features are prone to ambiguity, and thus inaccurate extraction often occurs during 3D point cloud processing. The accuracy of local feature extraction in the technologies still needs to be improved.